Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)
What is AI?
Artificial intelligence generally refers to processes and algorithms that are able to simulate human intelligence, including mimicking cognitive functions such as perception, learning and problem solving. Machine learning and deep learning (DL) are subsets of AI.
Specific practical applications of AI include modern web search engines, personal assistant programs that understand spoken language, self-driving vehicles and recommendation engines, such as those used by Spotify and Netflix.
There are four levels or types of AI—two of which we have achieved, and two which remain theoretical at this stage.
4 types of AI
Reactive machines are able to perform basic operations based on some form of input. At this level of AI, no “learning” happens—the system is trained to do a particular task or set of tasks and never deviates from that. These are purely reactive machines that do not store inputs, have any ability to function outside of a particular context, or have the ability to evolve over time.
Limited memory AI systems are able to store incoming data and data about any actions or decisions it makes, and then analyze that stored data in order to improve over time. This is where “machine learning” really begins, as limited memory is required in order for learning to happen.
Since limited memory AIs are able to improve over time, these are the most advanced AIs we have developed to date. Examples include self-driving vehicles, virtual voice assistants and chatbots.
What is ML?
In a nutshell, machine learning is a subset of AI that falls within the “limited memory” category in which the AI (machine) is able to learn and develop over time.
There are a variety of different machine learning algorithms, with the three primary types being supervised learning, unsupervised learning and reinforcement learning.
3 types of ML
Supervised learning is the simplest of these, and, like it says on the box, is when an AI is actively supervised throughout the learning process. Researchers or data scientists will provide the machine with a quantity of data to process and learn from, as well as some example results of what that data should produce (more formally referred to as inputs and desired outputs).
The result of supervised learning is an agent that can predict results based on new input data. The machine may continue to refine its learning by storing and continually re-analyzing these predictions, improving its accuracy over time.
Supervised machine learning applications include image-recognition, media recommendation systems, predictive analytics and spam detection.
What is DL?
Deep learning (DL) is a subset of machine learning that attempts to emulate human neural networks, eliminating the need for pre-processed data. Deep learning algorithms are able to ingest, process and analyze vast quantities of unstructured data to learn without any human intervention.
As with other types of machine learning, a deep learning algorithm can improve over time.
Some practical applications of deep learning currently include developing computer vision, facial recognition, and natural language processing.
Our approach towards AI, ML and DL
1. Automated Machine Learning Project Builder
2. Machine Learning Model Development
3. Edge Device Model Development
4. Exploratory data analysis
5. Statistical analysis and mathematical modelling
6. Chat Bots and Integrations